Modeling the Impacts of Sea Level Rise in Coastal Virginia at Multiple Scales

Relative sea level is increasing along the Mid-Atlantic coast of the United States and the rate of relative sea level rise (ΔRSL) for Coastal Virginia is approximately double the rate of global sea level rise (ΔSLRG)(1). The potential impacts posed to communities by ΔRSL are best understood by examining the spatial relationship between the upper limits of ocean-connected waters and the geographic positioning of critical natural and societal assets. This research examines this problem at three spatial scales to quantify the impacts of ΔRSL and storm flooding events on (i) structural and transportation infrastructure for the tide-influenced coastal zone of Virginia, (ii) physical and socioeconomic assets in Hampton Roads, and (iii) critical infrastructure at Port of Virginia’s Norfolk International Terminal South (NITS). Spatial modeling of future sea level rise produced data and maps of potential inundation and provided an assessment of impacts to land areas, roadways, and buildings throughout coastal Virginia. The total land area predicted to be inundated by sea level rise was 424 square miles (682 km2) in 2040, 534 square miles (859 km2) in 2060, and 649 square miles (1044 km2) in 2080. Modeling of a Category 1 hurricane (like Florence in 2018) making landfall near Virginia Beach and travelling westward through Hampton Roads with future ΔRSL of +1.5 feet (.46 m) and +3 feet (.91 m) predicted significant flooding and physical damages, including impairment to critical emergency services such as police, fire, and emergency medical transport. Modeling of hurricane storm surge with future ΔRSL to predict potential flooding at Port of Virginia’s NITS facility proved to be an effective screening tool for estimating current and future risk to critical facilities. Modeling revealed a near-linear pattern of vulnerability wherein the surface area predicted to be inundated by storms of identical category progressively increased as sea level increased. The multi-scale, -source, and -temporal techniques developed in this inundation modeling research provide data and replicable methodologies that others may use as a proven platform to calculate potential losses of natural resource, property, economy, and life resulting from inundation resulting from ΔRSL